Deep analytic model for student dropout prediction in massive open online courses. (July 2021)

• Record Type: Journal Article
• Authors: Mubarak, Ahmed A.; Cao, Han; Hezam, Ibrahim M.
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Convolutional neural networks (CNNs) -- Long short-term memory (LSTM) -- Class imbalance -- Cost-sensitive -- Dropout prediction -- Feature extraction -- Massive Open Online Courses (MOOCs); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107271
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Merging the CNN model with the LSTM model as a predictive model called CONV-LSTM. Extracting the automated features from dataset raw logs. Considering the influence of class imbalance on prediction in the big databases. Using a cost-sensitive technique in the loss function, which considers the various misclassification costs for false negatives and false positives. Comparing the model with other existing models in term the predicting learners' performance. Abstract: Predicting students' performance is critical in Massive Open Online Courses (MOOCs) in order to benefit from many aspects such as students' retention and make timely interventions. In this paper, we propose a hyper-model of Convolutional Neural Networks and Long Short-Term Memory, called CONV-LSTM, in order to automatically extract features from MOOCs raw data and predict whether each student will drop out or complete courses. We consider class imbalance problem, which means that models will be biased to yield good results on the majority of class examples and poor results on the minority of class examples. In that case, model prediction is inaccurate, which means that the false negative rate is high. To reinforce better prediction performance, a cost-sensitive technique is used in the loss function, which considers the various misclassification costs for false negatives and false positives. The proposed model shows a better performance when compared to baseline methods. Graphical abstract: Image, graphical abstract